Why Dev Data Fails in Production: How to Prepare

Ever wondered what powers the world of #DataEngineering? 🚀 I recently came across a fantastic resource that demystifies the must-know terms in data engineering—and trust me, it’s a must-save for later! If ETL, data lakes, or data governance sound like jargon, you’ll appreciate these bite-sized explanations. Here are some highlights: 🔗 Data Pipelines: These automate the flow of data, ensuring it goes from source to destination with minimal fuss. Who hates manual data work as much as me? 🙋♂️ 📊 ETL (Extract, Transform, Load): The backbone of most data processes—it brings together data from different places, makes it usable, and stores it for analysis. Which ETL tool do you swear by? 🌊 Data Lake vs. Data Warehouse: One holds raw, flexible data (data lake), while the other optimizes for analytics with structured data (data warehouse). Which do you prefer for your projects? 🧽 Data Quality & Cleansing: High-quality, reliable data is non-negotiable. Any horror stories of bad data leading to bad decisions? ⏳ Data Orchestration & Real-time Processing: Automated workflows and instant insights are changing the game. Are your data systems prepared for real time? These concepts, plus others like data modeling, integration, partitioning, and metadata, are the building blocks for scalable and modern data solutions. Which of these terms was new to you, or do you find most challenging? Drop your thoughts or questions in the comments—let’s help each other level up our data chops! #DataEngineering #Analytics #DataManagement #LearningTogether

"𝗪𝗲'𝗿𝗲 𝗶𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁𝗶𝗻𝗴 𝗗𝗮𝘁𝗮 𝗠𝗲𝘀𝗵 𝘁𝗼 𝘀𝗼𝗹𝘃𝗲 𝗼𝘂𝗿 𝗱𝗮𝘁𝗮 𝗽𝗿𝗼𝗯𝗹𝗲𝗺𝘀." I hear this constantly. And it's usually followed by exactly the same anti-patterns that made their data lake a graveyard. Here's what I'm seeing in enterprise after enterprise: 𝗗𝗼𝗺𝗮𝗶𝗻 𝗖𝗼𝗻𝗳𝘂𝘀𝗶𝗼𝗻 Teams spend months defining "domains" based on org charts instead of actual business value flows. Then they wonder why the data doesn't align with how work actually gets done. 𝗧𝗵𝗲 𝗜𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗶𝗼𝗻 𝗧𝗿𝗮𝗽 Data Mesh becomes the new ETL. Teams create "data products" that are really just APIs moving operational data between systems. This isn't analytics architecture – it's expensive middleware. 𝗣𝗹𝗮𝘁𝗳𝗼𝗿𝗺 𝗣𝗿𝗼𝗹𝗶𝗳𝗲𝗿𝗮𝘁𝗶𝗼𝗻 Every team builds their own data stack because "decentralization." Six months later, you have 12 different security models, incompatible data formats, and nobody who can support any of it. 𝗚𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲 𝗯𝘆 𝗛𝗼𝗽𝗲 "We'll establish governance as we go." Translation: "We'll deal with compliance and security after we get hacked or audited." Sound familiar? 𝗜𝘁'𝘀 𝘁𝗵𝗲 𝗱𝗮𝘁𝗮 𝗹𝗮𝗸𝗲 𝗽𝗹𝗮𝘆𝗯𝗼𝗼𝗸 𝘄𝗶𝘁𝗵 𝗻𝗲𝘄 𝗯𝘂𝘇𝘇𝘄𝗼𝗿𝗱𝘀. The uncomfortable truth: Your data problems aren't technical. They're organizational. • You don't know who owns what data • You can't define what a "product" actually is • Your teams lack the platform skills to be autonomous • You have no governance framework for federated decisions Data Mesh can work. But only if you fix the foundations first: ✓ 𝗗𝗼𝗺𝗮𝗶𝗻 𝗼𝘄𝗻𝗲𝗿𝘀𝗵𝗶𝗽 aligned with business reality, not IT structure ✓ 𝗔𝗰𝘁𝘂𝗮𝗹 𝗽𝗿𝗼𝗱𝘂𝗰𝘁𝘀 that deliver value to real customers ✓ 𝗣𝗹𝗮𝘁𝗳𝗼𝗿𝗺 𝗰𝗮𝗽𝗮𝗯𝗶𝗹𝗶𝘁𝗶𝗲𝘀 that enable teams without requiring PhD-level expertise ✓ 𝗚𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲 𝗴𝘂𝗮𝗿𝗱𝗿𝗮𝗶𝗹𝘀 established before teams need them 𝗧𝗵𝗲 𝗾𝘂𝗲𝘀𝘁𝗶𝗼𝗻 𝗲𝘃𝗲𝗿𝘆 𝗖𝗧𝗢 𝘀𝗵𝗼𝘂𝗹𝗱 𝗮𝘀𝗸: 𝗔𝗿𝗲 𝘄𝗲 𝗯𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝗮 𝗗𝗮𝘁𝗮 𝗠𝗲𝘀𝗵 𝗼𝗿 𝗷𝘂𝘀𝘁 𝗱𝗶𝘀𝘁𝗿𝗶𝗯𝘂𝘁𝗶𝗻𝗴 𝗼𝘂𝗿 𝗱𝗮𝘁𝗮 𝘀𝘄𝗮𝗺𝗽? Parallaxis #DataMesh #DataGovernance #PlatformEngineering

𝐁𝐮𝐢𝐥𝐝𝐢𝐧𝐠 𝐒𝐮𝐜𝐜𝐞𝐬𝐬𝐟𝐮𝐥 𝐃𝐚𝐭𝐚 𝐒𝐲𝐬𝐭𝐞𝐦𝐬 𝐒𝐭𝐚𝐫𝐭𝐬 𝐰𝐢𝐭𝐡 𝐀𝐫𝐜𝐡𝐢𝐭𝐞𝐜𝐭𝐮𝐫𝐞 Every great data system begins with a blueprint. That blueprint is Data Architecture — the essential outline that ensures efficiency, reliability, security, and cost-effectiveness. In simple terms: ➊ Data Architects design the blueprint. ➋ Data Engineers bring that design to life. Together, they create systems that truly serve the organization. ⸻ 𝐖𝐡𝐚𝐭 𝐆𝐨𝐨𝐝 𝐃𝐚𝐭𝐚 𝐀𝐫𝐜𝐡𝐢𝐭𝐞𝐜𝐭𝐮𝐫𝐞 𝐃𝐞𝐥𝐢𝐯𝐞𝐫𝐬 • 𝐏𝐞𝐫𝐟𝐨𝐫𝐦𝐚𝐧𝐜𝐞 & 𝐒𝐜𝐚𝐥𝐚𝐛𝐢𝐥𝐢𝐭𝐲 – Faster, future-proof systems • 𝐐𝐮𝐚𝐥𝐢𝐭𝐲 & 𝐑𝐞𝐥𝐢𝐚𝐛𝐢𝐥𝐢𝐭𝐲 – Clean, accurate, consistent data • 𝐂𝐨𝐬𝐭 𝐄𝐟𝐟𝐢𝐜𝐢𝐞𝐧𝐜𝐲 – Less time & money spent managing data • 𝐒𝐞𝐜𝐮𝐫𝐢𝐭𝐲 & 𝐆𝐨𝐯𝐞𝐫𝐧𝐚𝐧𝐜𝐞 – Compliance and protection by design ⸻ 𝐊𝐞𝐲 𝐀𝐫𝐞𝐚𝐬 𝐃𝐚𝐭𝐚 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐬 𝐒𝐡𝐨𝐮𝐥𝐝 𝐌𝐚𝐬𝐭𝐞𝐫 • Sources – Databases, data lakes, streaming feeds • ETL/ELT – Choosing frameworks & building efficient pipelines • Storage Patterns – Understanding the right fit (warehouse, lake, hybrid) • End Users – Designing for the people who will use the data ⸻ 𝐖𝐡𝐲 𝐈𝐭 𝐌𝐚𝐭𝐭𝐞𝐫𝐬 Good architecture helps data engineers: ✔ Select the right tech stack ✔ Design reliable, efficient pipelines ✔ Implement normalized, user-friendly data models ✔ Apply governance policies that safeguard the organization’s data ⸻ 𝐐𝐮𝐞𝐬𝐭𝐢𝐨𝐧𝐬 𝐓𝐨 𝐀𝐬𝐤 𝐁𝐞𝐟𝐨𝐫𝐞 𝐁𝐮𝐢𝐥𝐝𝐢𝐧𝐠 How will multiple sources be integrated? Is our data accurate and complete? Which models and tools fit best? How do we pipeline, partition, and secure data? How will the system scale tomorrow? Addressing these questions upfront helps data engineers create systems that not only work today but also grow with the business tomorrow. #DataEngineering #DataArchitect #DataArchitecture #Data

2025-09-05 :: The latest Agile Data Engineering pattern is out, this one is all about Data Matching / Data Diif / Data Reconciliation. #AgileDataEngineering #Patterns https://lnkd.in/gEdebpm6 When Nigel Vining and I used to do data project work as consultants we would always end up building a "Data Match" shortcut. Its one of those things we wouldn't use all the time, but when we needed it to trouble shoot a data problem it saved us a lot of time and effort. So Data Match is the next data engineering pattern we decided to document and share. You probably don't need to make the Data Match pattern as a reusable "product" feature, but you probably want to have the scaffolding code to do something similar in your data engineering toolkit. "you won't need it every night but you will need it"

The Semantic Layer: Your Data's Universal Translator 🔍 Ever wondered why different teams in your organisation get different answers from the same data? Enter the semantic layer – the unsung hero of modern data architecture. What is a Semantic Layer? Think of it as a universal translator between your raw data and business users. It's an abstraction layer that sits between your data sources and analytics tools, defining business logic, metrics, and relationships in one centralised location. Why Do You Need One? 🎯 Single Source of Truth: No more "which revenue number is correct?" debates. One definition, consistent everywhere. ⚡ Speed to Insight: Business users can self-serve without writing SQL or waiting for data teams. Analysts focus on analysis, not data preparation. 🔒 Governance & Security: Centralized access controls and data lineage. Know who's accessing what, when, and why. 📈 Scale with Confidence: As your data grows, your business logic remains consistent across all tools and teams. How Does It Work? The magic happens in three layers: Data Foundation → Raw data from warehouses, lakes, and operational systems Semantic Modeling → Business logic, calculations, relationships, and governance rules Consumption Layer → BI tools, applications, and APIs consume standardized metrics Popular tools making this happen: dbt Semantic Layer, LookML,Cube,AtScale, and emerging players like Transform and Metriql. The Bottom Line A well-implemented semantic layer transforms your data from a source of confusion into a competitive advantage. It's not just about better dashboards – it's about enabling data-driven decisions at scale. What's your experience with semantic layers? Are you building one, evaluating options, or still wrestling with inconsistent metrics? #DataArchitecture #Analytics #DataEngineering #SemanticLayer #DataStrategy

🔷 DATA STRATEGY & CONCEPTUAL DATA ARCHITECTURE LINKAGE Align strategy with architecture to turn vision into governed data products.   Ensures consistency goals to model design, improving agility. 🟡 DATA STRATEGY 🟢 Vision & Objectives: Define north-star metrics (time-to-insight, cost targets).   🔵 Governance: Assign stewardship roles; enforce audit trails.   🟠 Policies: Set retention, encryption, compliance standards.   🔴 Business Alignment: Co-create roadmaps with Finance, Risk, and IT.   🟣 Emerging Capabilities: Enable AI readiness through curated datasets.   🟤 Stakeholder Engagement: Host workshops to validate use cases. 🟠 CONCEPTUAL ARCHITECTURE 🟢 Domains & Entities: Customer, Policy, Claim, Transaction.   🔵 Relationships: Cardinality rules; hierarchies.   🟣 Flows & Models: Batch ETL, real-time streams, canonical schemas.   🔴 Entity Definitions: Standardize naming and attributes.   🟡 Data Flows: Design event-driven pipelines and batch processes.   🔵 Canonical Models: Define standard schemas for cross-domain integration. 🟢 LINKAGES 🟡 Goals → Domains: Map KPIs to scope.   🔴 Policies → Flows: Drive integration patterns.   🔵 Governance → Relationships: Ensure integrity.   🟠 Alignment → Entities: Derive models from workflows. ⚪ PILLARS 🟡 Data Quality: Profiling and validation rules.   🔴 Metadata Management: Business glossary and lineage.   🔵 Integration: API-first and event-driven design.   🟢 Security & Privacy: Encryption, RBAC and PII masking. Discuss best practices below.  

𝗪𝗵𝘆 𝗗𝗮𝘁𝗮 𝗠𝗲𝘀𝗵 𝗠𝗮𝘁𝘁𝗲𝗿𝘀? As data engineers, we've all felt the pain of scaling a centralized data platform. In the classic monolithic setup, one central team is expected to manage everything: ingestion, ETL, analytics, and every schema tweak in between. As organizations grow, this model breaks down fast:  • Approval backlogs pile up, slowing progress for everyone.  • CI/CD pipelines become sluggish and error-prone as complexity grows.  • Schema changes upstream can trigger chaos downstream.  • Business context gets lost, domain experts are disconnected from the data powering their decisions. 𝗧𝗵𝗲 𝗳𝘂𝗻𝗱𝗮𝗺𝗲𝗻𝘁𝗮𝗹 𝘀𝗵𝗶𝗳𝘁 Data Mesh flips the script by distributing both ownership and accountability to the teams who actually understand their data. It’s built on four engineering principles: 1. Domain-oriented ownership: Data products are owned and operated by the teams closest to the business. 2. Data as a product: Data gets the same focus on quality, SLAs, and usability as any customer-facing feature. 3. Self-serve data platform: Teams get the tools and autonomy to build, deploy, and monitor their own pipelines. 4. Federated governance: Global policies (privacy, lineage, access control) are enforced via automation, not manual gatekeeping. 𝗪𝗵𝗮𝘁 𝗖𝗵𝗮𝗻𝗴𝗲𝘀 𝗳𝗼𝗿 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 𝗧𝗲𝗮𝗺𝘀?  • Bottlenecks disappear: Each team ships and iterates on their own pipelines, models, and data products, no more central queue.  • Stronger contracts: Published data products have versioned, well-defined schemas. Downstream teams build on solid ground.  • Faster innovation: Independent teams mean rapid delivery from idea to production.  • True accountability: Data issues are traced back to owners, not a faceless central team. Has your team started moving toward Data Mesh? What challenges or wins have you seen along the way? #DataEngineering #DataMesh #ModernDataStack #Scalability

What’s the Best Data Architecture Today? It depends—on a lot. * Team capacity, capability, and skill set * Integration capabilities of source/target systems * Investment appetite * Business goals and timelines * Volume, velocity, and type of data * Regulatory compliance requirements * Stakeholder preferences * Scalability needs * Enforcement of naming standards and data hygiene Architecture options range from: * Legacy tools * Cutting-edge platforms * Stabilized modern stacks * Custom-built vs open-source ecosystems So how do you choose? Does one-size fit all? Not in my experience. Choosing the right architecture means aligning with your north star—then assessing every factor above with discipline. But here’s the real challenge: the tech landscape shifts constantly, and most teams don’t fully consider the downstream impacts of their decisions. Especially in today’s agile, siloed product teams, each group builds independently. It makes sense organizationally—but for data teams, it’s a nightmare. Integration becomes a burden. Data engineers, analysts, scientists, and other teams are left stitching together fragmented systems just to tell a coherent story. To meet deadlines, corners get cut. Each cut chips away at data quality and completeness. And who feels it most? The final consumer—business decision makers—who get frustrated when data is slow, inaccurate, or incomplete. I’ve lived this. At one point, I wanted to name our data lake: LakeErie. My better angel team convinced me LakeSuperior was friendlier. But the frustration was real. Real-time data was flowing—but riddled with upstream inconsistencies. How do we get ahead of this? Start with enterprise-wide Data Governance. * Define standards (e.g., phone number formats) * Bake them into the product teams creating the data * Build automated tests to enforce them * Use consistent naming conventions across systems * It is much harder putting the genie back in the bottle, once it is out in the wild. When governance is embedded early, data flows cleanly. And when it flows cleanly, it empowers—not frustrates. If you start at the source, your options of architecture and tools start to open up dramatically.

New ISG Software Research Analyst Perspective: Nextdata Provides a Platform for Distributed Data I previously described data mesh as a cultural and organizational approach to distributed data ownership, access and governance, rather than a product that could be acquired or even a technical architecture that could be built. While that remains true, many data management software providers have adapted their products in recent years to address the four key principles of data mesh: domain-oriented ownership, data as a product, self-serve data infrastructure and federated governance. New data management providers have also emerged with products built around these principles, including Nextdata, which is led by the originator of the data mesh concept.

Petition to stop using vague terms in data engineering. I see many teams label roles in pipelines as “owners,” “stakeholders,” or “users.” But these words rarely explain who actually does what. Who fixes a failing pipeline? Who gets alerts when data is delayed or corrupted? Who approves schema changes? Who maintains transformations or joins? If your policies or documentation can’t answer these questions clearly, they won’t work in practice. That’s why I advocate using precise terms like data producers and data consumers. These describe actual behavior, not abstract roles. A data producer is any system, team, or individual responsible for creating, generating, or modifying data. This includes manual data entry, ETL pipelines, API ingestion, or applications writing to databases. A data consumer is any person, process, or tool that uses data for downstream purposes. This includes analysts building dashboards, ML models using features, finance teams preparing reports, or business systems making decisions based on data. Clear language leads to clear responsibility, faster troubleshooting, and more reliable pipelines. Which vague data engineering term should we retire next?